





import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''

 def __init__(self							:  Optional[int]			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  str=1_024			,					UpperCAmelCase_							:  List[str]=1_024			,					UpperCAmelCase_							:  List[Any]=3.6)			->Dict:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =tokenizer
   lowerCamelCase__: Tuple         =tokenizer.bos_token_id
   lowerCamelCase__: Optional[Any]         =dataset
   lowerCamelCase__: str         =seq_length
   lowerCamelCase__: Union[str, Any]         =seq_length * chars_per_token * num_of_sequences







 def __iter__(self							:  List[str])			->int:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =iter(self.dataset)
   lowerCamelCase__: Dict         =True
   while more_examples:
     lowerCamelCase__    ,   lowerCamelCase__: Optional[int]         =[], 0
     while True:
       if buffer_len >= self.input_characters:
         break
       try:
         buffer.append(next(UpperCAmelCase_)["content"])
         buffer_len += len(buffer[-1])
       except StopIteration:
         lowerCamelCase__: Optional[int]         =False
         break
     lowerCamelCase__: Dict         =tokenizer(UpperCAmelCase_			,					truncation=UpperCAmelCase_)["input_ids"]
     lowerCamelCase__: Any         =[]
     for tokenized_input in tokenized_inputs:
       all_token_ids.extend(tokenized_input + [self.concat_token_id])
     for i in range(0			,					len(UpperCAmelCase_)			,					self.seq_length):
       lowerCamelCase__: str         =all_token_ids[i : i + self.seq_length]
       if len(UpperCAmelCase_) == self.seq_length:
         yield torch.tensor(UpperCAmelCase_)





def 	lowerCAmelCase_					(			__a						)	->						List[str]:







  """simple docstring"""





  lowerCamelCase__: str         ={"streaming": True}
  lowerCamelCase__: Union[str, Any]         =load_dataset(args.dataset_name      ,       split="train"      ,       **__a						)
  lowerCamelCase__: str         =ConstantLengthDataset(__a      ,       __a      ,       seq_length=args.seq_length						)
  lowerCamelCase__: str         =DataLoader(__a      ,       batch_size=args.batch_size						)
  return eval_dataloader





def 	lowerCAmelCase_					(			__a						)	->						Tuple:







  """simple docstring"""





  model.eval()
  lowerCamelCase__: int         =[]
  for step, batch in enumerate(__a						):
    with torch.no_grad():
      lowerCamelCase__: Tuple         =model(__a      ,       labels=__a						)
    lowerCamelCase__: Union[str, Any]         =outputs.loss.repeat(args.batch_size						)
    losses.append(accelerator.gather(__a						)						)

    if args.max_eval_steps > 0 and step >= args.max_eval_steps:
      break
  lowerCamelCase__: Union[str, Any]         =torch.mean(torch.cat(__a						)						)
  try:
    lowerCamelCase__: Any         =torch.exp(__a						)
  except OverflowError:
    lowerCamelCase__: Optional[int]         =float("inf"						)
  return loss.item(), perplexity.item()


# Setup Accelerator
__A							   =							Accelerator()

# Parse configuration
__A							   =							HfArgumentParser(EvaluationArguments)
__A							   =							parser.parse_args()
set_seed(args.seed)

# Logging
__A							   =							logging.getLogger(__name__)
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)

# Load model and tokenizer
__A							   =							AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A							   =							AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__A							   =							create_dataloader(args)

# Prepare everything with our `accelerator`.
__A   ,      __A							   =							accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
__A   ,      __A							   =							evaluate(args)






logger.info(f'loss/eval: {eval_loss}, perplexity: {perplexity}')









"""simple docstring"""





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						complex						,							_lowerCamelCase			:						str = "x"						,							_lowerCamelCase			:						float = 10**-10						,							_lowerCamelCase			:						int = 1						,							) ->							complex:
  lowerCamelCase_       	=		symbols(_lowerCamelCase					)
  lowerCamelCase_       	=		lambdify(_lowerCamelCase						,							_lowerCamelCase					)
  lowerCamelCase_       	=		lambdify(_lowerCamelCase						,							diff(_lowerCamelCase						,							_lowerCamelCase					)					)

  lowerCamelCase_       	=		starting_point

  while True:
    if diff_function(_lowerCamelCase					) != 0:
      lowerCamelCase_       	=		prev_guess - multiplicity * func(_lowerCamelCase					) / diff_function(
          _lowerCamelCase					)
    else:
      raise ZeroDivisionError('Could not find root'					) from None

    # Precision is checked by comparing the difference of consecutive guesses
    if abs(next_guess - prev_guess					) < precision:
      return next_guess

    lowerCamelCase_       	=		next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(F'''The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}''')

  # Find root of polynomial
  # Find fourth Root of 5
  print(F'''The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}''')

  # Find value of e
  print(
      '''The root of log(y) - 1 = 0 is ''',
      F'''{newton_raphson('log(y) - 1', 2, variable='y')}''',
  )

  # Exponential Roots
  print(
      '''The root of exp(x) - 1 = 0 is''',
      F'''{newton_raphson('exp(x) - 1', 10, precision=0.005)}''',
  )

  # Find root of cos(x)
  print(F'''The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}''')




